| MADGAN: Unsupervised medical anomaly detection GAN using multiple adjacent brain MRI slice reconstruction C Han, L Rundo, K Murao, T Noguchi, Y Shimahara, ZÁ Milacski, ... BMC bioinformatics 22 (2), 1-20, 2021 | 63 | 2021 |
| Robust Detection of Anomalies via Sparse Methods ZÁ Milacski, M Ludersdorfer, A Lorincz, P van der Smagt 22nd International Conference on Neural Information Processing, 2015 | 13 | 2015 |
| GAN-based multiple adjacent brain MRI slice reconstruction for unsupervised Alzheimer’s disease diagnosis C Han, L Rundo, K Murao, ZÁ Milacski, K Umemoto, E Sala, H Nakayama, ... International Meeting on Computational Intelligence Methods for …, 2019 | 10 | 2019 |
| Columnar Machine: Fast estimation of structured sparse codes A Lőrincz, ZÁ Milacski, B Pintér, AL Verő Biologically Inspired Cognitive Architectures 15, 19-33, 2016 | 9 | 2016 |
| Towards reasoning based representations: Deep consistence seeking machine A Lőrincz, M Csákvári, Á Fóthi, ZÁ Milacski, A Sárkány, Z Tősér Cognitive Systems Research 47, 92-108, 2018 | 8 | 2018 |
| Iterative calibration method for microscopic road traffic simulators T Tettamanti, ZÁ Milacski, A Lőrincz, I Varga Periodica Polytechnica Transportation Engineering 43 (2), 87-91, 2015 | 7 | 2015 |
| Differentiable Unrolled Alternating Direction Method of Multipliers for OneNet ZÁ Milacski, B Póczos, A Lőrincz 30th British Machine Vision Conference (BMVC), 2019 | 6 | 2019 |
| Estimating cartesian compression via deep learning A Lőrincz, A Sárkány, ZÁ Milacski, Z Tősér International Conference on Artificial General Intelligence, 294-304, 2016 | 5 | 2016 |
| Cost and risk sensitive decision making and control for highway overtaking maneuver A Mihály, ZÁ Milacski, Z Tősér, A Lőrincz, T Tettamanti, I Varga 2015 International conference on models and technologies for intelligent …, 2015 | 4 | 2015 |
| Group k-Sparse Temporal Convolutional Neural Networks: Unsupervised Pretraining for Video Classification ZÁ Milacski, B Póczos, A Lőrincz 2019 International Joint Conference on Neural Networks (IJCNN), 2019 | 3 | 2019 |
| Multi object tracking for similar instances: a hybrid architecture Á Fóthi, KB Faragó, L Kopácsi, ZÁ Milacski, V Varga, A Lőrincz International Conference on Neural Information Processing, 436-447, 2020 | 2 | 2020 |
| Declarative description: The meeting point of artificial intelligence deep neural networks and human intelligence Z Milacski, K Faragó, A Fóthi, V Varga, A Lorincz XAI 2018, Proceedings of the 2nd Workshop on Explainable Artificial …, 2018 | 2 | 2018 |
| Cognitive deep machine can train itself A Lőrincz, M Csákvári, Á Fóthi, ZÁ Milacski, A Sárkány, Z Tősér arXiv preprint arXiv:1612.00745, 2016 | 2 | 2016 |
| Speech de-identification with deep neural networks Á Fodor, L Kopácsi, ZÁ Milacski, A Lőrincz Acta Cybernetica 25 (2), 257-269, 2021 | 1 | 2021 |
| Adversarial perturbation stability of the layered group basis pursuit D Szeghy, ZA Milacski, A Fóthi, A Lorincz def 1, 2, 2021 | 1 | 2021 |
| CNN‐based MRI regression using u‐net C HAN, F GESSER, Z Milacski | 1 | 2018 |
| Enhancing Crowdsourced Applications via Incorporated Practice Sessions. KB Faragó, ZA Milacski, A Nemeth, A Lörincz GamifIR@ ECIR, 41-43, 2015 | 1 | 2015 |
| Structural Extensions of Basis Pursuit: Guarantees on Adversarial Robustness D Szeghy, M Aslan, Á Fóthi, B Mészáros, ZÁ Milacski, A Lőrincz arXiv preprint arXiv:2205.08955, 2022 | | 2022 |
| Temporal Reconstruction Methods in Self-Supervised Machine Learning ZÁ Milacski | | 2021 |
| VideoOneNet: bidirectional convolutional recurrent onenet with trainable data steps for video processing Z Milacski, B Póczos, A Lorincz International Conference on Machine Learning, 6893-6904, 2020 | | 2020 |
Lorincz, AndrasEotvos Lorand UniversityE-mail megerősítve itt: inf.elte.hu
